Classification Models for Skin Tumor Detection Using Texture Analysis in Medical Images

Author:

Almeida Marcos A. M.ORCID,Santos Iury A. X.ORCID

Abstract

Medical images have made a great contribution to early diagnosis. In this study, a new strategy is presented for analyzing medical images of skin with melanoma and nevus to model, classify and identify lesions on the skin. Machine learning applied to the data generated by first and second order statistics features, Gray Level Co-occurrence Matrix (GLCM), keypoints and color channel information—Red, Green, Blue and grayscale images of the skin were used to characterize decisive information for the classification of the images. This work proposes a strategy for the analysis of skin images, aiming to choose the best mathematical classifier model, for the identification of melanoma, with the objective of assisting the dermatologist in the identification of melanomas, especially towards an early diagnosis.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Computer Vision and Pattern Recognition,Radiology, Nuclear Medicine and imaging

Reference28 articles.

1. https://www.wcrf.org/dietandcancer/cancer-trends/skin-cancer-statistics

2. Félix Castro-Espinozaa and Volodymyr Ponomaryov. An Intelligent System for the Diagnosis of Skin Cancer on Digital Images taken with Dermoscopy;Fernandez;Acta Polytech. Hung.,2017

3. Use of Statistical Techniques to Analyze Textures in Medical Images for Tumor Detection and Evaluation;Almeida;Adv. Mol. Imaging Interv. Radiol.,2018

4. https://www.iarc.fr

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